Advances in Data-Driven Modeling, Fault Detection, and Fault Identification

Capture the future of resilient engineering with Advances in Data-Driven Modeling, Fault Detection, and Fault Identification by Mohamed N. Nounou, Hazem N. Nounou, Nour Basha, and Byanne Malluhi. This authoritative volume speaks directly to engineers, researchers, and decision-makers seeking practical, cutting-edge approaches to keeping complex systems safe and productive.

Explore a comprehensive suite of modern techniques — from statistical learning and signal processing to state-of-the-art machine learning and system identification — all tailored to the demands of fault detection and fault identification in industrial systems. Clear explanations and applied examples make advanced concepts accessible whether you’re optimizing process plants, designing predictive maintenance programs, or safeguarding critical infrastructure.

Imagine reducing downtime, lowering maintenance costs, and improving safety across manufacturing, energy, transportation, and utility sectors. This book shows how to convert sensor data into actionable insight: building reliable data-driven models, diagnosing anomalies robustly, and isolating root causes swiftly. Emphasis on model validation, uncertainty quantification, and real-world implementation ensures the methods translate from research to results.

Ideal for control engineers, data scientists, academics, and operations managers in North America, Europe, Asia, the Middle East, and beyond, this resource blends theory with practice to support global engineering challenges. Whether you’re updating a graduate curriculum or leading a digital transformation at an industrial site, this text equips you with the tools to detect faults earlier and respond smarter.

A strategic investment for teams focused on predictive maintenance and advanced process monitoring — advance your capabilities and protect your assets with insights from leading experts in the field.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.